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46 lines
3.2 KiB
Markdown
46 lines
3.2 KiB
Markdown
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# SARSA: An Introduction to Reinforcement Learning
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Reinforcement Learning (RL) is a subfield of machine learning concerned with training agents to make decisions in an environment, maximizing a notion of cumulative reward. One popular RL method is **SARSA**, which stands for State-Action-Reward-State-Action. SARSA is an on-policy, model-free control algorithm with applications ranging from robotics to game playing.
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## The Basic Idea
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SARSA utilizes a table, often called a Q-table, to estimate the value of each state-action pair. The Q-table maps the state-action pairs to a numeric value representing the expected cumulative reward. The algorithm aims to learn the optimal policy, which is the sequence of actions that yields the highest cumulative reward over time.
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## The SARSA Algorithm
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The SARSA algorithm is relatively simple to understand, making it a popular choice for introductory RL tutorials. Here is a step-by-step breakdown of the algorithm:
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1. Initialize the Q-table with small random values.
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2. Observe the current state **s**.
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3. Choose an action **a** using an exploration-exploitation trade-off strategy (such as ε-greedy).
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4. Perform the chosen action **a** in the environment.
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5. Observe the reward **r** and the new state **s'**.
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6. Choose a new action **a'** for the new state **s'** using the same exploration-exploitation strategy.
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7. Update the Q-table value for the state-action pair **(s, a)** using the update rule:
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```
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Q(s,a) = Q(s,a) + α⋅[R + γ⋅Q(s',a') - Q(s,a)]
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```
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where:
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- **α** is the learning rate, controlling the weight given to the new information.
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- **R** is the observed reward for the state-action pair.
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- **γ** is the discount factor, determining the importance of future rewards.
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8. Set the current state and action to the new state and action determined above (i.e., **s = s'** and **a = a'**).
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9. Repeat steps 2 to 8 until the agent reaches a terminal state or a predefined number of iterations.
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## Advantages and Limitations
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SARSA has several advantages that contribute to its popularity:
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- Simplicity: SARSA is relatively easy to understand and implement, making it a great starting point for beginners.
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- On-policy: It learns and improves the policy it follows while interacting with the environment, making it robust to changes in policy during training.
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- Works with continuous state and action spaces: Unlike some other RL algorithms, SARSA can handle continuous state and action spaces effectively.
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However, SARSA also has a few limitations:
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- Less efficient for large state spaces: SARSA's reliance on a Q-table becomes impractical when the state space is exceptionally large, as it would require significant memory resources.
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- Struggles with high-dimensional or continuous action spaces: SARSA struggles in situations where the number of possible actions is large or continuous, as the action-state value function becomes difficult to approximate accurately.
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## Conclusion
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SARSA is a fundamental reinforcement learning algorithm that provides an introduction to the field. Although it may have limitations in certain scenarios, SARSA is a valuable tool with various applications. As machine learning research continues to evolve, SARSA's simplicity and intuition make it an essential algorithm for studying reinforcement learning.
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